Hello HN,
I’m a solo developer building tools for real estate workflows. I built OfferGridAI after watching listing agents repeatedly struggle with the same problem during hot markets.
When a property gets multiple offers, each offer usually comes in as a 10–20 page PDF. Under tight time pressure, agents have to manually dig through each document and rebuild a spreadsheet to compare things like price, net to seller, contingencies, financing, closing timeline, escalation clauses, etc. It’s not conceptually hard, but it’s stressful, time-consuming, and easy to miss details buried deep in the PDFs.
I wanted a way to make that moment less chaotic.
The idea:
Upload multiple offer PDFs → extract the key terms → generate a clean, side-by-side comparison grid that’s easy to walk through with a seller.
Instead of just dumping text, the tool normalizes the information into comparable fields (price vs net, contingencies, financing strength, days to close) and adds a short summary highlighting tradeoffs (e.g. highest price vs highest certainty to close).
What it focuses on:
Structured extraction of common purchase-agreement terms
Normalizing offers so sellers can compare apples to apples
Surfacing risk factors (financing type, contingencies, timeline)
Producing a seller-ready grid rather than raw AI output
What it intentionally does not do:
Make decisions for agents or sellers
Replace professional judgment
Integrate with MLS or transaction management systems (at least for now)
The goal is to be a fast decision-support tool for a very specific, high-pressure moment.
I’m early and still refining the scope, especially around:
Which fields matter most in practice
How to communicate “risk” without over-claiming
How tolerant users are of “best effort” extraction vs perfection
I’d love feedback from anyone who’s worked with complex PDFs, document comparison, or decision-support tools under time pressure, or from anyone who’s built vertical SaaS in heavily regulated industries.
Happy to answer questions and learn from the community.
When a property gets multiple offers, each offer usually comes in as a 10–20 page PDF. Under tight time pressure, agents have to manually dig through each document and rebuild a spreadsheet to compare things like price, net to seller, contingencies, financing, closing timeline, escalation clauses, etc. It’s not conceptually hard, but it’s stressful, time-consuming, and easy to miss details buried deep in the PDFs.
I wanted a way to make that moment less chaotic.
The idea: Upload multiple offer PDFs → extract the key terms → generate a clean, side-by-side comparison grid that’s easy to walk through with a seller.
Instead of just dumping text, the tool normalizes the information into comparable fields (price vs net, contingencies, financing strength, days to close) and adds a short summary highlighting tradeoffs (e.g. highest price vs highest certainty to close).
What it focuses on:
Structured extraction of common purchase-agreement terms
Normalizing offers so sellers can compare apples to apples
Surfacing risk factors (financing type, contingencies, timeline)
Producing a seller-ready grid rather than raw AI output
What it intentionally does not do:
Make decisions for agents or sellers
Replace professional judgment
Integrate with MLS or transaction management systems (at least for now)
The goal is to be a fast decision-support tool for a very specific, high-pressure moment.
I’m early and still refining the scope, especially around:
Which fields matter most in practice
How to communicate “risk” without over-claiming
How tolerant users are of “best effort” extraction vs perfection
I’d love feedback from anyone who’s worked with complex PDFs, document comparison, or decision-support tools under time pressure, or from anyone who’s built vertical SaaS in heavily regulated industries.
Happy to answer questions and learn from the community.